Generalisation of continuous models to estimate soil characteristics into similar delineations of a detailed soil map
M. H. Salehi A C , Z. Safaei A , I. Esfandiarpour-Borujeni B and J. Mohammadi A
+ Author Affiliations
- Author Affiliations
A Department of Soil Science, College of Agriculture, Shahrekord University, Shahrekord, Iran.
B Department of Soil Science, College of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.
C Corresponding author. Email: mehsalehi@yahoo.com
Soil Research 51(4) 350-361 https://doi.org/10.1071/SR12221
Submitted: 6 August 2012 Accepted: 31 July 2013 Published: 2 September 2013
Abstract
The aim of soil mapping is to partition soil bodies using map units, which are more homogenous for specific soil properties than are the soil bodies as a whole. Soil properties are expected to be similar at delineations of a specified soil map unit. Therefore, it is supposed that a model developed to estimate a soil property for one of these delineations could be generalised for the others. This study was conducted to determine the possibility of generalisation (extrapolation) of continuous models of spatial variability to estimate soil physical and chemical properties in similar delineations of a soil map unit. A consociation soil map unit in two different locations of a detailed soil map (1 : 20 000 scale), as similar delineations, was selected in the north-west of Faradonbeh region, Iran. Sixty topsoil samples (0–20 cm) were randomly collected in each delineation (totally 120 samples) with 30-m intervals and the samples were GPS-recorded. Laboratory studies consisted of bulk density, pH, calcium-carbonate equivalent, organic matter content, percentage of coarse fragments, and particle-size distribution. First, variography was done according to the soil data of each delineation (named areas A and B) and kriged maps were generated based on their own semivariogram parameters. Then, the kriged map of the soil properties for the second similar delineation (area B) was regenerated based on the corresponding models and their parameters obtained from the first similar delineation (area A). Finally, the regenerated kriged map of each variable was compared with its original kriged map. Visual comparison of the kriged maps of area B obtained from two steps of variography showed very high accordance for all of the soil properties. Quantitative comparison of the kriged maps suggests that the accuracy expected by the users of the soil information should be considered before generalisation of the data for similar units. Lower values of accordance obtained by the Kappa index and, especially, the classification success index than overall accuracy indicate that model generalisation should not be used where high precision of soil information is expected. Discrepancies observed for the kriged maps of the same variables in similar delineations could be due to different soil management practices in the past as a result of different historical developments.
Additional keywords: continuous models, geostatistics, Kappa index, overall accuracy, soil map delineations.
References
Atkinson PM (1991) Optimal ground-based sampling for remote sensing investigations. Estimating the regional mean. International Journal of Remote Sensing 12, 559–567.| Optimal ground-based sampling for remote sensing investigations. Estimating the regional mean.Crossref | GoogleScholarGoogle Scholar |
Ayoubi S, Mohammad Zamani S, Khormali F (2007) Spatial variability of some soil properties for site specific farming in northern Iran. International Journal of Plant Production 1, 1735–6814.
Bailey TC, Gatrell AC (1998) ‘Interactive spatial data analysis.’ (Addison Wesley Longman: London)
Brus DJ, De Gruijter JJ (1997) Random sampling or geostatistical modeling? Choosing between design-based and model-based sampling strategies for soil (with Discussion). Geoderma 80, 1–44.
| Random sampling or geostatistical modeling? Choosing between design-based and model-based sampling strategies for soil (with Discussion).Crossref | GoogleScholarGoogle Scholar |
Cambardella CA, Karlen DK (1999) Spatial analysis of soil fertility parameters. Precision Agriculture 1, 5–14.
| Spatial analysis of soil fertility parameters.Crossref | GoogleScholarGoogle Scholar |
Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58, 1501–1511.
| Field-scale variability of soil properties in central Iowa soils.Crossref | GoogleScholarGoogle Scholar |
Casey P, Altobelli G, Pignatelli P (2009) Application of the hypothesis analysis method using Cohen’s Kappa index to measure the agreement between leather sorters. Journal of the Society of Leather Technology and Chemists (JSLTC) 94, 144–149.
Cohen J (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20, 37–46.
| A coefficient of agreement for nominal scales.Crossref | GoogleScholarGoogle Scholar |
Esfandiarpoor Borujeni I, Salehi MH, Toomanian N, Mohammadi J, Poch RM (2009) The effect of survey density on the results of geopedological approach in soil mapping: a case study in the Borujen region, Central Iran. Catena 79, 18–26.
| The effect of survey density on the results of geopedological approach in soil mapping: a case study in the Borujen region, Central Iran.Crossref | GoogleScholarGoogle Scholar |
Farifteh J, Van der Meer F, Atzberger C, Carranza EJM (2007) Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN). Remote Sensing of Environment 110, 59–78.
| Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN).Crossref | GoogleScholarGoogle Scholar |
Fenton TE, Lauterbach MA (1998) Soil map unit composition and scale of mapping related to interpretations for precision soil and crop management in Iowa. In ‘Proceedings of the 4th International Conference on Precision Agriculture’. (Eds PC Robert, RH Rust, WE Larson) pp. 239–251. (Soil Science Society of America: Madison, WI)
Fleiss JL (1981) ‘Statistical methods for rates and proportions’. 2nd edn (John Wiley & Sons Inc.: New York)
Geypens M, Vanongeval L, Vogels N, Meykens J (1999) Spatial variability of agricultural soil fertility parameters in a Gleyic Podzol of Belgium. Precision Agriculture 1, 319–326.
| Spatial variability of agricultural soil fertility parameters in a Gleyic Podzol of Belgium.Crossref | GoogleScholarGoogle Scholar |
Golden Software Inc. (2002) Surfer 8.00. Golden Software Inc., Golden, CO, USA.
Goovaerts P (1997) ‘Geostatistics for natural resources evaluation.’ (Oxford University Press: New York)
Hartemink AE (2008) Soil map density and a nation’s wealth and income. In ‘Digital soil mapping with limited data’. (Eds AE Hartemink, AB McBratney, ML Mendonça-Santos) pp. 53–66. (Springer: Dordrecht, The Netherlands)
Hengl T, Huvelink GBM, Stein A (2004) A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120, 75–93.
| A generic framework for spatial prediction of soil variables based on regression-kriging.Crossref | GoogleScholarGoogle Scholar |
ITC (2009) ILWIS 3.7 for Windows. ITC, Enschede, The Netherlands.
Koukoulas S, Blackburn GA (2001) Introduction new indices for accuracy evaluation of classified images representing semi-natural woodland environments. Photogrammetric Engineering and Remote Sensing 67, 499–510.
Krasilnikov P, Carré F, Montanarella L (Eds) (2008) ‘Soil geography and geostatistics: Concepts and applications.’ Joint Research Centre (JRC) Scientific and Technical Reports. (European Commission: Luxembourg)
Kravchenko AN (2003) Influence of spatial structure on accuracy of interpolation methods. Soil Science Society of America Journal 67, 1564–1571.
| Influence of spatial structure on accuracy of interpolation methods.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXnsVGlu7c%3D&md5=52a83457f3da635af78640bd663a1af3CAS |
Legros JP (2006) ‘Mapping of the soil.’ (Translated from the French by VAK Sarma.) (Science Publishers: Enfield, NH)
Lin H, Wheeler D, Bell J, Wilding L (2005) Assessment of soil spatial variability at multiple scales. Ecological Modeling 182, 271–290.
| Assessment of soil spatial variability at multiple scales.Crossref | GoogleScholarGoogle Scholar |
Liu TL, Juang KW, Lee DY (2006) Interpolation of soil properties using kriging combined with categorical information of soil maps. Soil Science Society of America Journal 70, 1200–1209.
| Interpolation of soil properties using kriging combined with categorical information of soil maps.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XmvVWqsbo%3D&md5=232bab0b069395d2d698f56d24f6f2eaCAS |
López-Granados F, Jurado-Expósito M, Atenciano S, García-Ferrer A, Sánchez de la Orden M, García-Torres L (2002) Spatial variability of agricultural soil parameters in southern Spain. Plant and Soil 246, 97–105.
| Spatial variability of agricultural soil parameters in southern Spain.Crossref | GoogleScholarGoogle Scholar |
Mallavan BP, Minasny B, McBratney AB (2010) Homosoil, a methodology for quantitative extrapolation of soil information across the globe. In ‘Digital soil mapping’. Progress in Soil Science 2. (Eds JL Boettinger et al.) pp. 137–149. (Springer: Berlin)
Marchant BP, Lark RM (2007) Optimized sample schemes for geostatistical surveys. Mathematical Geology 39, 113–134.
| Optimized sample schemes for geostatistical surveys.Crossref | GoogleScholarGoogle Scholar |
McBratney AB, Pringle MJ (1999) Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precision Agriculture 1, 125–152.
| Estimating average and proportional variograms of soil properties and their potential use in precision agriculture.Crossref | GoogleScholarGoogle Scholar |
McBratney AB, Webster R (1981) The design of optimal sampling schemes for local estimation and mapping of regionalized variables-II. Computers & Geosciences 7, 335–365.
| The design of optimal sampling schemes for local estimation and mapping of regionalized variables-II.Crossref | GoogleScholarGoogle Scholar |
McBratney AB, Odeh IOA, Bishop TFA, Dunbar MS, Shatar TM (2000) An overview of pedometric techniques for use in soil survey. Geoderma 97, 293–327.
| An overview of pedometric techniques for use in soil survey.Crossref | GoogleScholarGoogle Scholar |
McBratney AB, Minasny B, Cattle S, Vervoort RW (2002) From pedotransfer functions to soil inference systems. Geoderma 109, 41–73.
| From pedotransfer functions to soil inference systems.Crossref | GoogleScholarGoogle Scholar |
McBratney AB, Mendonc Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117, 3–52.
| On digital soil mapping.Crossref | GoogleScholarGoogle Scholar |
Mueller TG, Pusuluri NB, Mathias KK, Cornelius PL, Barnhisel RI, Shearer SA (2004) Map quality for ordinary kriging and inverse distance weighted interpolation. Soil Science Society of America Journal 68, 2042–2047.
| Map quality for ordinary kriging and inverse distance weighted interpolation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXpvVCku7k%3D&md5=0e3561165b8cbecff49a676818b6a63fCAS |
Nordt LC, Jacob JS, Wilding LP (1991) Quantifying map unit composition for quality control in soil survey. In ‘Spatial variabilities of soils and landform’. Soil Science Society of America Special Publication No. 28. (Eds MJ Mausbach, LP Wilding) pp. 183–197. (Soil Science Society of America: Madison, WI)
Odeh IOA, McBratney AB, Chittleborough DJ (1990) Design of optimal sample spacing for mapping soil using fuzzy-k-means and regionalized variable theory. Geoderma 47, 93–122.
| Design of optimal sample spacing for mapping soil using fuzzy-k-means and regionalized variable theory.Crossref | GoogleScholarGoogle Scholar |
Pannatier Y (1996) ‘VARIOWIN 2.2: Software for spatial data analysis in 2D.’ (Springer: Berlin)
Park SJ, Vlek PLG (2002) Environmental correlation of three-dimensional soil spatial variability: a comparison of three adaptive techniques. Geoderma 109, 117–140.
| Environmental correlation of three-dimensional soil spatial variability: a comparison of three adaptive techniques.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XltV2rurg%3D&md5=2cd9a91583dcfeca40418161d2d79d15CAS |
Prescott JA (1938) The climate of tropical Australia in relation to possible agricultural occupation. Transactions of the Royal Society of South Australia 62, 229–240.
Rahman S, Munn LC, Zhang R, Vance GF (1996) Rocky mountain forest soils: evaluating spatial variability using conventional statistics and geostatistics. Canadian Journal of Soil Science 76, 501–507.
| Rocky mountain forest soils: evaluating spatial variability using conventional statistics and geostatistics.Crossref | GoogleScholarGoogle Scholar |
Reese RE, Moorhead KK (1996) Spatial characteristics of soil properties along an elevational gradient in a Carolina bay wetland. Soil Science Society of America Journal 60, 1273–1277.
| Spatial characteristics of soil properties along an elevational gradient in a Carolina bay wetland.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK28Xktl2qtrc%3D&md5=916477f4af6fa50ebdb380be2598eb3fCAS |
Resende M, Curi N, Rezende SBD, Corrêa GF (2002) ‘Pedology: a base for differentiating environments.’ (University of Vicosa, NEPUT: Vicosa, Brazil) [in Portuguese]
Rossiter DG (2000) ‘Methodology for soil resource inventories. Lecture notes.’ 2nd revised version. (Soil Science Division, International Institute for Aerospace Survey and Earth Science (ITC): Enschede, The Netherlands)
Salehi MH, Eghbal MK, Khademi H (2003) Comparison of soil variability in a detailed and a reconnaissance soil map in central Iran. Geoderma 111, 45–56.
| Comparison of soil variability in a detailed and a reconnaissance soil map in central Iran.Crossref | GoogleScholarGoogle Scholar |
Salehi MH, Khademi H, Eghbal MK (2004) Stable isotope geochemistry of carbonates and organic carbon in selected soils from Chaharmahal Bakhtiari province, Iran. Communications in Soil Science and Plant Analysis 35, 1681–1697.
| Stable isotope geochemistry of carbonates and organic carbon in selected soils from Chaharmahal Bakhtiari province, Iran.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXksFSgsb0%3D&md5=92b96203778f7b5e8545d5442e77c3c0CAS |
Schloeder CA, Zimmerman NE, Jacobs MJ (2001) Comparison of methods for interpolating soil properties using limited data. Soil Science Society of America Journal 65, 470–479.
| Comparison of methods for interpolating soil properties using limited data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38Xpt1On&md5=ba4d70857c51889cc39b9ad87c4048baCAS |
Skidmore AK, Ryan PJ, Dawes W, Short D, O’Loughlin E (1991) Use of an expert system to map forest soils from a geographical information system. International Journal of Geographical Information Science 5, 431–445.
| Use of an expert system to map forest soils from a geographical information system.Crossref | GoogleScholarGoogle Scholar |
Soil Survey Division Staff (1993) ‘Soil survey manual.’ Handbook No. 18. (USDA-NRCS: Washington, DC)
Soil Survey Staff (1996) Soil survey laboratory methods manual. Report No. 42. USDA, NRCS, NCSS, Washington, DC.
Soil Survey Staff (2010) ‘Keys to Soil Taxonomy.’ 11th edn (USDA-NRCS: Washington, DC)
Stutter MI, Langan SJ, Lumsdon DG, Clark LM (2009) Multi-element signatures of stream sediments and sources under moderate to low flow conditions. Applied Geochemistry 24, 800–809.
| Multi-element signatures of stream sediments and sources under moderate to low flow conditions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXltVWgu7c%3D&md5=c4b96d3dc9b19ed9154acced9eb778f4CAS |
Thomas PJ, Baker JC, Zelazny LW, Hatch DR (2000) Relationship of map unit variability to shrink–swell indicators. Soil Science Society of America Journal 64, 262–268.
| Relationship of map unit variability to shrink–swell indicators.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3cXmslyhtL8%3D&md5=d621e7060a16e920999912b2d3005c2eCAS |
Trangmar BB, Yost RS, Uehara G (1986) Application of geostatistics to spatial studies of soil properties. Advances in Agronomy 38, 45–94.
| Application of geostatistics to spatial studies of soil properties.Crossref | GoogleScholarGoogle Scholar |
USEPA (1991) GEOEAS (Geostatistical Environmental Assessment Software). US Environmental Protection Agency, Environmental Monitoring Systems Laboratory Office of Research and Development Las Vegas, NV, USA.
Warrick AW, Myers DE, Nielsen DR (1986) Geostatistical methods applied to soil science. In ‘Methods of soil analysis, Part 1. Physical and mineralogical methods’. Agronomy Monograph No. 9. 2nd edn. pp. 53–82. (American Society of Agronomy: Madison, WI)
Webster R, Oliver MA (1992) Sample adequately to estimate variograms of soil properties. Journal of Soil Science 43, 177–192.
| Sample adequately to estimate variograms of soil properties.Crossref | GoogleScholarGoogle Scholar |
Wilding LP (1985) Spatial variability: its documentation, accommodation and implication to soil surveys. In ‘Soil spatial variability’. (Eds DR Nielsen, J Bouma) pp. 166–194. (Pudoc: Wageningen, The Netherlands)
Yemefack M, Rossiter DG, Njomgang R (2005) Multi-scale characterization of soil variability within an agricultural landscape mosaic system in southern Cameroon. Geoderma 125, 117–143.
| Multi-scale characterization of soil variability within an agricultural landscape mosaic system in southern Cameroon.Crossref | GoogleScholarGoogle Scholar |
Young FJ, Hammer RD, Larson D (1999) Frequency distributions of soil properties on a loess-mantled Missouri watershed. Soil Science Society of America Journal 63, 178–185.
| Frequency distributions of soil properties on a loess-mantled Missouri watershed.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1MXitFGgtr0%3D&md5=8654183016cf23a44035cdda6706bab9CAS |
Zinck JA (1989) Physiography and soils. Lecture notes for soil students. Soil Science Division, Soil Survey Courses Subject Matter, K6. ITC, Enschede, The Netherlands.
